Virtual-real fusion test method of ice breaker thermodynamic system for polar region complex working conditions

Abstract

The invention belongs to the technical field of icebreaker thermodynamic systems, and particularly relates to a virtual-real fusion test method of an icebreaker thermodynamic system for polar complex working conditions, which comprises the following steps: S1, building a real object experiment system of the icebreaker thermodynamic system; s2, based on a mechanism model principle, constructing a virtual system of a thermodynamic system of the icebreaker; s3, performing a physical experiment under the typical working condition of the polar region by using a physical experiment system; performing a simulation experiment of the virtual system by using the same typical working conditions of the polar region; the virtual system is corrected based on the experimental data; s4, using the corrected virtual system to perform a simulation experiment of the polar region complex working condition to obtain corresponding simulation experiment data; and S5, carrying out design optimization and control strategy optimization on the thermodynamic system of the icebreaker by using the simulation data and the experiment data obtained in the S3 and the S4. According to the method, the dynamic characteristics of the system can be clearly revealed, the modeling accuracy is improved, the limitation of experimental tests is overcome, and an effective auxiliary means for system design optimization is provided.

Description

Technical Field

[0001] The invention belongs to the technical field of the thermal system of icebreakers, and particularly relates to a virtual-real fusion test method for the thermal system of icebreakers facing polar complex working conditions. Background Art

[0002] Under the background of global warming, the Arctic ice sheet is gradually melting, making the Arctic region increasingly important in terms of resource development, maritime transportation, etc. As one of the key equipment, the thermal system of icebreakers is responsible for converting the thermal energy of steam into mechanical energy or electrical energy, and its complexity and coupling pose extremely high requirements for design. Facing extreme working conditions such as continuous cyclic shocks, surfacing icebreaking, long-term large inclination angles, and continuous large swings, traditional thermal system models are difficult to accurately reflect the performance of the thermal system of icebreakers under these conditions and cannot meet the needs of design optimization.

[0003] Existing thermal system simulation and modeling technologies usually rely on the comparative analysis of simulation results and limited test data. However, due to factors such as site, manpower, financial resources, and time, it is almost an impossible task to conduct full-condition tests for all system design schemes. For example, during the icebreaking process, the rapid change in the power of the power system may make it difficult to directly measure the dynamic response characteristics of the fluid system and equipment. In addition, the acquisition of experimental data is also affected by various factors such as equipment accuracy, operator skill level, and environmental conditions, which further reduces the accuracy and consistency of the data.

[0004] The main problems faced by the current technology include: 1) the static and dynamic characteristics of the thermal system of icebreakers under polar complex working conditions are not clear; 2) traditional thermal system modeling methods and dynamic models cannot meet the research needs; 3) experimental tests are difficult to carry out comprehensively due to various restrictive factors; 4) there is a lack of effective auxiliary means to carry out on-line evaluation and optimization of the flexibility of the thermal system of icebreakers. The existence of these problems is mainly attributed to the complexity and variability of the system operating conditions under polar working conditions and the lack of effective test verification means. Solving these problems not only requires an in-depth understanding of the behavior of the system under different working conditions, but also requires the development of a new method that can combine physical experiments and virtual simulations to overcome the limitations of existing technologies and resources.

[0005] Therefore, how to clearly reveal the dynamic characteristics of the system, improve the modeling accuracy, overcome the limitations of experimental tests, and provide an effective auxiliary means for system design optimization has become an urgent problem to be solved at present. Summary of the Invention

[0006] Aiming at the deficiencies of the above-mentioned existing technologies, the invention provides a virtual-real fusion test method for the thermal system of icebreakers facing polar complex working conditions, which can clearly reveal the dynamic characteristics of the system, improve the modeling accuracy, overcome the limitations of experimental tests, and provide an effective auxiliary means for the design optimization of the thermal system of icebreakers.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions:

[0008] A virtual-real fusion test method for the thermal system of an icebreaker facing polar complex working conditions, comprising the following steps:

[0009] S1. Based on the composition structure of the actual thermal system of the icebreaker, build a physical experimental system of the icebreaker with a typical thermal system;

[0010] S2. Based on the theoretical basis of the mechanism model, construct a dynamic model of the typical thermal system of the icebreaker; taking each functional device of the system as a basic unit, construct a virtual system of the thermal system of the icebreaker; the composition structure of the virtual system is the same as that of the physical experimental system;

[0011] S3. Use the physical experimental system to conduct physical experiments under typical polar working conditions; at the same time, use the same typical polar working conditions to conduct simulation experiments on the virtual system; and based on the experimental data of the physical experiment and the simulation experiment, correct the virtual system model to achieve an accurate description of the virtual system for the physical system;

[0012] S4. Use the corrected virtual system to conduct simulation experiments under polar complex working conditions to obtain corresponding simulation experimental data;

[0013] S5. Use the simulation data and experimental data obtained in S3 and S4 to optimize the design and control strategy of the thermal system of the icebreaker.

[0014] Specifically in implementation, the optimization design includes the optimization of the system flexibility structure and the optimization of the system control strategy. That is, through the virtual-real fusion test in the present invention, carry out system characteristic tests and analysis research under high-frequency load lifting and lowering in polar icebreaking working conditions, as well as system power rapid matching research, so as to provide an effective solution for the rapid power matching of the system under high-frequency load lifting and lowering.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] 1. Clearly reveal the dynamic characteristics of the system. Through the combination of physical experiments and virtual simulations, especially conducting simulation experiments under polar complex working conditions, it is possible to deeply understand the actual operating conditions of the thermal system of the icebreaker under extreme conditions. This includes not only the understanding of the performance of individual functional devices, but also the coupling behavior of the entire system. Compared with the traditional method that only relies on theoretical analysis or limited on-site tests, this method provides a more comprehensive and accurate system dynamic model and research method.

[0017] 2. Improve the modeling accuracy. Continuously correct the virtual model based on the physical experiment data, ensuring the accuracy and reliability of the model. Especially after preliminary verification under typical polar working conditions, further use the corrected model to simulate the complex polar working conditions, improving the credibility of the prediction results. Compared with the existing technology, this method significantly reduces the errors caused by inaccurate initial assumptions and improves the overall accuracy of the model.

[0018] 3. Overcome the limitations of experimental tests. Physical experiments are often limited by factors such as site, cost, and safety, and it is difficult to fully cover all possible working conditions. Virtual simulation can flexibly adjust parameter settings and simulate various actual operating conditions, making up for the deficiencies of physical experiments. Compared with the existing technology, this method effectively solves the problem that comprehensive experiments cannot be carried out due to resource limitations, and realizes extensive testing of the thermal system of the icebreaker under different working conditions.

[0019] 4. Provide an effective auxiliary means for system design optimization. Using the corrected virtual system for detailed analysis and evaluation can identify potential problems and improvement points in the system, providing a scientific basis for subsequent design optimization. Compared with traditional methods, this method can not only discover existing problems, but also explore the optimal solution by simulating different design schemes, greatly enhancing the efficiency and effectiveness of the design process.

[0020] In summary, this method can clearly reveal the dynamic characteristics of the system, improve the modeling accuracy, overcome the limitations of experimental tests, and provide an effective auxiliary means for system design optimization. It not only makes up for the deficiencies of traditional technologies in dealing with complex polar working conditions, but also through the way of integrating virtual and physical, raises the research, development and optimization of the thermal system of the icebreaker to a new level, providing strong technical support for future Arctic exploration.

[0021] Preferably, in S2, the process of constructing the virtual system of the thermal system of the icebreaker includes:

[0022] Step 2.1: Based on the physical experiment data and mechanism analysis, identify the key working medium parameters that affect the performance of the thermal system and the operating state of the equipment, and form a set of working medium state parameters that characterize the dynamic characteristics of the system; through the physical experiment data and mechanism analysis, select the key parameters that can reflect the preset dimensions to form a set of equipment state parameters; the preset dimensions include the energy-mass transfer efficiency and fatigue stress of the equipment.

[0023] Step 2.2: Based on the set of working medium state parameters, adopt a modular modeling method, and starting from the preset angles, establish a mathematical model for describing the working medium state parameters and their change processes for the thermal system and its functional equipment, as the corresponding working medium dynamic model; the preset angles include mass, energy, momentum, and the physical properties of the working medium, and the preset motion forms include rotation, translation, and vibration.

[0024] Step 2.3: Based on the set of equipment state parameters and their variation laws, considering the preset motion forms, establish control equations for the functional equipment of each non-fluid container, and combine the boundary conditions and constraint conditions to construct a dynamic model of the functional equipment; the preset operation forms include rotation, translation, and vibration; for each typical fluid container equipment in the system, construct its three-dimensional dynamic model;

[0025] Step 2.4: Integrate the working medium dynamic model and the dynamic model. Taking each functional equipment as a basic unit, integrate the system-level dynamic model with multi-physical field coupling according to the actual working medium flow path and signal transmission relationship in the thermal system, as the virtual system of the thermal system.

[0026] With such a setting, 1. accurately identify key parameters. The establishment of the system equipment state parameter set is based on physical test data and mechanism analysis, ensuring the accurate capture of key factors affecting system performance and equipment operation. This provides a solid data foundation for subsequent modeling and helps improve the authenticity and reliability of the model. Compared with the method relying only on theoretical derivation, this method can more accurately reflect the system behavior under actual operating conditions.

[0027] 2. Modular modeling improves the flexibility and accuracy of the model. Adopt a modular approach to construct the working medium dynamic model from dimensions such as mass, energy, momentum, and working medium physical properties, enabling each functional equipment to be analyzed and optimized independently, and also facilitating model update and expansion. This method not only improves the flexibility of the model but also enhances its ability to adapt to different working conditions, which is conducive to in-depth understanding of the interaction between components and its impact on the overall system.

[0028] 3. Comprehensively cover the dynamic characteristics of equipment. For the functional equipment of non-fluid containers, consider motion forms such as rotation, translation, and vibration, and establish control equations in combination with boundary conditions and constraint conditions; for fluid containers, construct a three-dimensional dynamic model. This method fully considers the complex dynamic behavior of equipment in actual operation, improving the accuracy of simulation. Compared with traditional single-dimensional or simplified models, this method can provide a more comprehensive and detailed description of equipment behavior, helping to discover potential design defects or improvement spaces.

[0029] 4. Integrate the system-level dynamic model with multi-physical field coupling. Finally, integrate the fluid network model and the equipment dynamic model into a complete system-level dynamic model, and organize it according to the actual working medium flow path and signal transmission relationship. This integration method not only reflects the overall architecture of the system but also supports the study of the interaction between multi-physical fields. Compared with traditional separate modeling, the system-level dynamic model can better simulate the complex situations in the real world and provides strong support for the optimal design of the system.

[0030] Therefore, through a series of carefully designed steps, this method effectively establishes a virtual model of the icebreaker's thermal system, achieving a clear revelation of the system's dynamic characteristics, a significant improvement in modeling accuracy, an effective overcoming of experimental test limitations, and providing a powerful tool for system design optimization. It not only deepens the understanding of the internal operation mechanism of the system but also lays a foundation for the efficient and reliable operation of the thermal system in future Arctic expeditions and icebreaking missions.

[0031] Preferably, in step 2.1, the construction process of the set of state parameters includes:

[0032] Based on physical test data and mechanism analysis, measurable core parameters of the working medium that affect the output of the thermal system and the operation of equipment are screened to form a set of parameters for characterizing dynamic characteristics; the measurable core parameters of the working medium include temperature, pressure, and flow rate.

[0033] Such a setting: 1. Ensures that the basic data of the model has high accuracy and relevance. Compared with methods that rely on data from a single source or assumptions, this process can more comprehensively reflect the system behavior under actual operating conditions and provides solid data support for subsequent modeling.

[0034] 2. Combining the characteristics of the icebreaking working conditions, especially the impact of high-frequency load fluctuations (such as icebreaking cycle shocks, floating icebreaking) on the state parameters of the working medium, is analyzed emphatically. This step helps to identify the unique challenges and change patterns that the system may encounter under extreme conditions. This in-depth analysis for specific application scenarios improves the adaptability of the model to special working conditions and makes the prediction results closer to the actual situation, which is particularly important when facing complex and changeable polar working conditions.

[0035] Preferably, in step 2.2, the physical properties of the working medium include the equation of state and the viscosity model.

[0036] Such a setting, combined with the mass conservation equation, the energy conservation equation, and the momentum theorem, modularizes the entire thermal system and equipment for modeling, which not only improves the model's ability to reflect actual operating conditions but also enhances its adaptability to different working condition changes. This comprehensive consideration enables the model to better handle complex and changeable working conditions.

[0037] Preferably, in step 2.2, after establishing a mathematical model for describing the state parameters of the working medium and their change process, a high-frequency load dynamic response module is embedded in the mathematical model according to the characteristics of the icebreaking working conditions to depict the rapid change process of the working medium parameters; the high-frequency load dynamic response module includes a transient flow control equation and a phase change lag correction term.

[0038] With such a setting: 1. Enhance the adaptability to extreme working conditions. By embedding a high-frequency load dynamic response module, the mathematical model can more accurately reflect the performance of the icebreaker's thermal system when facing sudden changes in instantaneous load, such as sharp variations. This targeted design significantly enhances the model's adaptability to unexpected situations that may occur in the actual operating environment, making its prediction results closer to the actual situation.

[0039] 2. Precisely simulate transient processes. On the basis of fully considering the influence of the working medium state, a multi-degree-of-freedom attitude motion model of the system is constructed; and based on the energy and mass conservation theory of the working medium, a working medium energy and mass transfer and conversion model of the thermal system that can accurately reflect the dynamic characteristics of energy and mass and the influence of equipment posture is constructed. Compared with traditional static or quasi-static models, this method can provide a more detailed process description, contribute to the flexible structural optimization of the system, and instantly and accurately grasp the potential of the system's flexible operation, providing an effective solution for the rapid power matching of the system under high-frequency load fluctuations.

[0040] 3. Support the optimal design and fault prevention of the system. The more refined mathematical model provides a scientific basis for the optimal design of the icebreaker's thermal system, helping engineers identify and solve bottleneck problems in the system. At the same time, by deeply understanding the rapid change process of the working medium parameters, possible operation challenges or fault modes can be foreseen in advance, and corresponding preventive measures can be formulated to ensure the safe and stable operation of the system.

[0041] Preferably, in step 2.3, for each typical fluid container device, a fatigue stress model is further introduced to characterize the dynamic stress characteristics of the device under extreme working conditions; the fatigue stress model includes a thermal-mechanical coupling equation.

[0042] With such a setting: 1. Improve the accuracy of equipment durability assessment. The introduction of the fatigue stress model, especially including the thermal-mechanical coupling equation, enables more accurate simulation and assessment of the dynamic stress characteristics of the equipment under extreme working conditions. This accurate assessment helps identify areas of the equipment that may suffer fatigue damage, so as to take targeted improvement measures to improve the durability and reliability of the equipment.

[0043] 2. Comprehensively consider multiple stress factors. The thermal-mechanical coupling equation takes into account the influence of temperature changes on the mechanical properties of materials and the interaction between mechanical stress and thermal stress, which provides more comprehensive support for the analysis of equipment under complex working conditions. Compared with traditional methods that only consider a single stress source, this method can better reflect the true stress state of the equipment under actual operating conditions and provide more reliable prediction results.

[0044] 3. Optimization Design and Preventive Maintenance. By accurately simulating the stress distribution of the equipment under different working conditions, it is possible to guide the design optimization, reduce potential design defects, and extend the service life of the equipment. In addition, based on the data provided by the fatigue stress model, an effective preventive maintenance plan can be formulated to carry out necessary repairs or replacements in advance, reducing the risk of sudden failures and ensuring the continuous and stable operation of the system.

[0045] Preferably, in step 2.3, for typical fluid container equipment, a model construction method that combines the dimensionality reduction mechanism model and the data-driven model is adopted to construct its three-dimensional dynamic model.

[0046] Such a setting: 1. Improves the calculation efficiency. The dimensionality reduction method extracts key features from high-dimensional CFD simulation data to construct a low-dimensional reduced-order model, significantly reducing the amount of calculation and calculation time. This method enables complex fluid dynamics problems that originally required a large amount of computing resources to be solved quickly on an ordinary computer, greatly improving the efficiency of the simulation process.

[0047] 2. Achieves real-time performance while maintaining high accuracy. Based on the characteristics of the theoretical model and the data-driven model, a numerical calculation method combining explicit and implicit multi-time scales is developed to balance the calculation accuracy and real-time performance of the model, ensuring that the reduced-order model can accurately capture the main dynamic characteristics of the original three-dimensional model. In this way, while ensuring the accuracy of the simulation results, real-time monitoring and analysis of the system's dynamic behavior are achieved.

[0048] Preferably, in S3, the correction of the virtual system includes steady-state correction and transient correction;

[0049] Among them, the steady-state correction includes: under typical polar working conditions, calibrating the corresponding parameters in the virtual system by comparing the experimental data of physical experiments and simulation experiments;

[0050] The transient correction includes: applying typical transient excitations to verify the transient response accuracy of the dynamic model and calibrating the corresponding parameters in the virtual system; the typical transient excitations include load step changes and sudden ice resistance shocks; the transient response accuracy includes overshoot and settling time.

[0051] Such settings: 1. Improve the accuracy of the model. Steady-state correction: By comparing the data of physical experiments and simulation experiments under typical polar conditions, relevant parameters in the virtual system are calibrated. This method can ensure that the virtual model accurately reflects the performance of the actual system under stable operating conditions, improving the credibility of the basic model. Transient correction: Apply typical transient excitations (such as load step changes, sudden ice resistance shocks), and calibrate the parameters of the virtual system based on experimental data to verify and improve its transient response accuracy (including overshoot, settling time). This step enables the virtual model to provide reliable prediction results during dynamic changes as well.

[0052] 2. Enhance the reliability of the model. The combined action of steady-state and transient corrections ensures that the virtual model behaves as close to the actual situation as possible under different operating conditions. This enhances the overall reliability and applicability of the model, making it an important tool for studying and optimizing the thermal system of icebreakers. Compared with methods that rely solely on theoretical assumptions or limited test data, this comprehensive calibration strategy provides a more comprehensive and detailed description of system behavior.

[0053] 3. Support the simulation and analysis of complex working conditions. By accurately modeling the response to typical transient excitations, it is possible to better understand the operation of the icebreaker's thermal system under extreme conditions. This is crucial for evaluating the performance of the system when facing sudden situations. This ability provides a basis for designers to identify potential problems in the early design stage and take corresponding measures to improve the robustness and adaptability of the system.

[0054] Preferably, in S5, the optimization design includes the optimization of the system flexibility structure and the optimization of the system control strategy.

[0055] Such settings, the optimization of the thermal system flexibility structure and the optimization of the system control strategy implemented in step S5 can enhance the reliability, safety, and adaptability of the icebreaker's thermal system. This method not only ensures the efficient and stable operation of the system under extreme conditions but also provides a solid foundation for future continuous improvement and innovative development. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to make the objectives, technical solutions, and advantages of the invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings, where:

[0057] Figure 1 is the flowchart of this method. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0058] The following will be further described in detail through specific embodiments:

[0059] Example:

[0060] AsFigure 1 As shown, in this embodiment, a virtual-real fusion test method for the thermal system of an icebreaker facing polar complex working conditions is disclosed, including the following steps:

[0061] S1. Based on the composition structure of the actual icebreaker thermal system, build a physical experimental system for the icebreaker thermal system.

[0062] S2. Based on the principle of the mechanism model, construct the dynamic models of each functional device in the icebreaker thermal system; and taking each functional device as a basic unit, construct a virtual system for the icebreaker thermal system; the composition structure of the virtual system is the same as that of the physical experimental system.

[0063] Specifically, in S2, the process of constructing the virtual system of the icebreaker thermal system includes:

[0064] Step 2.1. Based on the physical test data and mechanism analysis, identify the key working fluid parameters that affect the performance of the thermal system and the operating state of the equipment, and form a set of working fluid state parameters that characterize the dynamic characteristics of the system; through the physical test data and mechanism analysis, select the key parameters that can reflect the preset dimensions to form a set of equipment state parameters; the preset dimensions include the energy-mass transfer efficiency and fatigue stress of the equipment.

[0065] Specifically, the energy-mass transfer efficiency dimension of the equipment includes the heat transfer coefficient and mechanical efficiency; the fatigue stress dimension includes the vibration amplitude and thermal stress. In this way, by focusing on these two core indicators of the heat transfer coefficient and mechanical efficiency, the energy conversion and transfer efficiency of each functional device in the icebreaker thermal system can be accurately evaluated. This detailed evaluation helps to identify the energy loss points in the system, optimize the energy utilization efficiency, and thus improve the overall performance of the system. In addition, considering the vibration amplitude and thermal stress as the key parameters for measuring the fatigue stress of the equipment, the wear conditions and potential failure risks of the equipment under different working conditions can be comprehensively monitored. Especially in the polar complex working conditions, this targeted monitoring is particularly important, because it can early warn of possible problems, guide the formulation of maintenance plans, reduce unexpected downtime, and extend the service life of the equipment.

[0066] Step 2.2. Based on the set of working fluid state parameters, adopt a modular modeling method, and for each functional device of the thermal system, starting from the preset angles, establish a mathematical model for describing the working fluid state parameters and their change processes as the corresponding working fluid dynamic model; the preset angles include mass, energy, momentum, and working fluid physical properties.

[0067] In specific implementation, the physical properties of the working fluid include the equation of state and the viscosity model. In this way, modular modeling by combining the equation of state and the viscosity model not only improves the ability of the model to reflect actual operating conditions but also enhances its adaptability to different working condition changes. This comprehensive consideration enables the model to better handle complex and variable working conditions.

[0068] Step 2.3: Based on the set of equipment state parameters and their variation rules, considering the preset motion forms, establish control equations for each functional device of non-fluid containers, and construct a dynamic model of the functional device in combination with boundary conditions and constraint conditions; the preset operating forms include rotation, translation, and vibration; for each typical fluid container device in the system, construct its three-dimensional dynamic model.

[0069] In specific implementation, for each typical fluid container device, a fatigue stress model is also introduced to characterize the dynamic stress characteristics of the device under extreme working conditions; the fatigue stress model includes a thermal-mechanical coupling equation. In this way, the accuracy of equipment durability assessment can be improved. The introduction of the fatigue stress model, especially including the thermal-mechanical coupling equation, enables more accurate simulation and evaluation of the dynamic stress characteristics of the device under extreme working conditions. This accurate assessment helps identify potential fatigue damage areas of the device, thereby taking targeted improvement measures to enhance the durability and reliability of the device. In addition, the thermal-mechanical coupling equation takes into account the influence of temperature changes on the mechanical properties of materials and the interaction between mechanical stress and thermal stress, which provides more comprehensive support for equipment analysis under complex working conditions. Compared with the traditional method that only considers a single stress source, this method can better reflect the true stress state of the device under actual operating conditions and provide more reliable prediction results. Moreover, by accurately simulating the stress distribution of the device under different working conditions, it can guide design optimization, reduce potential design defects, and extend the service life of the device. In addition, based on the data provided by the fatigue stress model, an effective preventive maintenance plan can be formulated to carry out necessary repairs or replacements in advance, reduce the risk of sudden failures, and ensure the continuous and stable operation of the system.

[0070] For typical fluid container equipment, a model construction method that combines a dimensionality reduction mechanism model and a data-driven model is adopted to construct its three-dimensional dynamic model, so as to comprehensively and immediately grasp the situation information of the fluid working medium. In this way, by using the dimensionality reduction method to extract key features from high-dimensional CFD simulation data, a low-dimensional reduced-order model is constructed, significantly reducing the amount of calculation and calculation time. This method enables complex fluid dynamics problems that originally required a large amount of computing resources to be solved quickly on ordinary computers, greatly improving the efficiency of the simulation process. At the same time, based on the characteristics of the theoretical model and the data-driven model, a numerical calculation method with explicit and implicit combined multi-time scales is developed to balance the calculation accuracy and real-time performance of the model, ensuring that the reduced-order model can accurately capture the main dynamic characteristics of the original three-dimensional model. Thus, while ensuring the accuracy of the simulation results, real-time monitoring and analysis of the system's dynamic behavior are achieved.

[0071] Step 2.4: Integrate the working medium dynamic model and the dynamic model. Taking each functional device as the basic unit, integrate the system-level dynamic model with multi-physical field coupling according to the actual working medium flow path and signal transmission relationship of the thermal system, as the virtual system of the thermal system.

[0072] Such a setting can accurately identify key parameters. The establishment of the working medium state parameter set and the equipment state parameter set is based on physical test data and mechanism analysis, ensuring the accurate capture of the key factors affecting system performance and equipment operation. This provides a solid data foundation for subsequent modeling and helps improve the authenticity and reliability of the model. Compared with the method that only relies on theoretical derivation, this method can more accurately reflect the system behavior under actual operating conditions. In addition, a modular method is used to construct the working medium dynamic model from dimensions such as mass, energy, momentum, and working medium physical properties, enabling each functional device to be analyzed and optimized independently, and also facilitating the update and expansion of the model. This method not only improves the flexibility of the model but also enhances its ability to adapt to different working conditions, which is conducive to in-depth understanding of the interaction between components and its impact on the overall system.

[0073] Moreover, for functional devices of non-fluid containers, considering motion forms such as rotation, translation, and vibration, control equations are established by combining boundary conditions and constraint conditions; for fluid containers, a three-dimensional dynamic model is constructed. This method fully considers the complex dynamic behavior of the device during actual operation, improving the accuracy of simulation. Compared with traditional single-dimensional or simplified models, this method can provide a more comprehensive and detailed description of the device behavior, helping to identify potential design flaws or improvement spaces. In addition, the working fluid dynamic model and the device dynamic model are finally integrated into a complete system-level dynamic model, which is organized according to the actual working fluid flow path and signal transmission relationship. This integration method not only reflects the overall architecture of the system but also supports the study of the interaction between multiple physical fields. Compared with traditional separate modeling, the system-level dynamic model can better simulate the complex situations in the real world, providing strong support for the optimal design of the system.

[0074] Through a series of carefully designed steps, a virtual model of the icebreaker thermal system is effectively established, achieving a clear revelation of the system's dynamic characteristics, a significant improvement in modeling accuracy, an effective overcoming of experimental test limitations, and providing a powerful tool for system design optimization. It not only deepens the understanding of the internal operation mechanism of the system but also lays a foundation for the efficient and reliable operation of the thermal system in future Arctic explorations and icebreaking missions.

[0075] During specific implementation, in step 2.1, the construction process of the working fluid state parameter set includes:

[0076] Based on physical test data and mechanism analysis, the core working fluid parameters that affect the output of the thermal system and the operation of the equipment are screened to form a set of working fluid dynamic characteristic representation parameters; the core working fluid parameters include temperature, pressure, flow rate, and phase change characteristics;

[0077] Combined with the characteristics of the icebreaking working condition, the influence of high-frequency load rise and fall on the working fluid state parameters is analyzed, and a working fluid state parameter set is obtained; the high-frequency load rise and fall include transient flow rate fluctuations and phase change delays.

[0078] In this way, the core working fluid parameters can be accurately captured. Through physical test data and mechanism analysis, the core working fluid parameters (such as temperature, pressure, flow rate, phase change characteristics) that affect the output of the thermal system and the operation of equipment are screened out, forming a set of characterization parameters for the dynamic characteristics of the working fluid. This method ensures that the basic data of the model has high accuracy and relevance. Compared with the method that relies on data from a single source or assumptions, this process can more comprehensively reflect the system behavior under actual operating conditions, providing solid data support for subsequent modeling. In addition, combined with the characteristics of the icebreaking working condition, especially the impact of high-frequency load fluctuations (such as transient flow rate fluctuations, phase change delays) on the state parameters of the working fluid is analyzed emphatically. This step helps to identify the unique challenges and change patterns that the system may encounter under extreme conditions. This in-depth analysis for specific application scenarios improves the adaptability of the model to special working conditions, making the prediction results closer to the actual situation, which is particularly important when facing complex and changeable polar working conditions.

[0079] During specific implementation, in step 2.2, after establishing a mathematical model for describing the state parameters of the working fluid and its change process, a high-frequency load dynamic response module is embedded in the mathematical model according to the characteristics of the icebreaking working condition to depict the rapid change process of the working fluid parameters; the high-frequency load dynamic response module includes a transient flow control equation and a phase change lag correction term.

[0080] In this way, the adaptability to extreme working conditions can be enhanced. By embedding a high-frequency load dynamic response module, the mathematical model can more accurately reflect the performance of the icebreaker's thermal system in the face of sudden changes in instantaneous load, such as sharp variations. This targeted design significantly enhances the model's adaptability to unexpected situations that may occur in the actual operating environment, making its prediction results closer to the actual situation. In addition, it can accurately simulate transient processes. The introduction of transient flow control equations allows the model to precisely capture the sharp changes in fluid flow within a short period. This is crucial for understanding the behavior of the system when the power demand suddenly increases during icebreaking operations. Compared with traditional static or quasi-static models, this method can provide a more detailed process description, helping to identify potential design flaws or optimization spaces. Moreover, the phase change lag correction term takes into account the possible delay phenomenon when a substance changes from one phase to another under rapid load change conditions. This correction improves the simulation accuracy of the phase change process, especially important under the complex working conditions in the polar regions. Accurately predicting the phase change lag helps to better manage the energy conversion efficiency, avoid energy losses caused by incomplete phase changes, and thus improve the overall performance of the system. It also supports the optimal design and fault prevention of the system. A more refined mathematical model provides a scientific basis for the optimal design of the icebreaker's thermal system, helping engineers identify and solve bottleneck problems in the system. At the same time, through in-depth understanding of the rapid change process of working fluid parameters, potential operation challenges or fault modes can be foreseen in advance, and corresponding preventive measures can be formulated to ensure the safe and stable operation of the system.

[0081] S3. Conduct physical experiments under typical polar working conditions using a physical experiment system; at the same time, conduct simulation experiments on the virtual system under the same typical polar working conditions; and correct the virtual system based on the experimental data from the physical experiments and simulation experiments.

[0082] During specific implementation, the correction of the virtual system includes steady-state correction and transient correction;

[0083] Among them, the steady-state correction includes: under typical polar working conditions, calibrate the corresponding parameters in the virtual system by comparing the experimental data from the physical experiments and simulation experiments;

[0084] The transient correction includes: applying typical transient excitations, verifying the transient response accuracy of the dynamic model, and calibrating the corresponding parameters in the virtual system; the typical transient excitations include load step changes and sudden ice resistance impacts; the transient response accuracy includes overshoot and settling time.

[0085] In this way, the accuracy of the model can be improved. Steady-state correction: By comparing the data of physical experiments and simulation experiments under typical polar conditions, relevant parameters in the virtual system are calibrated. This method can ensure that the virtual model accurately reflects the performance of the actual system under stable operating conditions, improving the credibility of the basic model. Transient correction: Apply typical transient excitations (such as load step changes, sudden ice resistance shocks), and calibrate the parameters of the virtual system based on experimental data to verify and improve its transient response accuracy (including overshoot, settling time). This step enables the virtual model to provide reliable prediction results during dynamic changes. It can also enhance the reliability of the model. The combined action of steady-state and transient corrections ensures that the virtual model performs as close to the actual situation as possible under different operating conditions. This enhances the overall reliability and applicability of the model, making it an important tool for studying and optimizing the thermal system of icebreakers. Compared with methods that rely solely on theoretical assumptions or limited test data, this comprehensive calibration strategy provides a more comprehensive and detailed description of system behavior. In addition, it supports the simulation and analysis of complex working conditions. By accurately modeling the response to typical transient excitations, the operation of the icebreaker's thermal system under extreme conditions can be better understood. This is crucial for evaluating the performance of the system in the face of sudden situations. This ability provides a basis for designers to identify potential problems in the early design stage and take corresponding measures to improve the robustness and adaptability of the system.

[0086] S4. Use the corrected virtual system to conduct simulation experiments on complex polar conditions (such as icebreaking cycle shocks, surfacing icebreaking, long-term large inclination, and large sway), and obtain corresponding simulation experiment data.

[0087] S5. Use the simulation experiment data obtained in S4 to optimize the design of the thermal system and control strategy of the icebreaker.

[0088] During specific implementation, the optimization design includes the optimization of the system flexibility structure and the optimization of the system control strategy. That is, through the virtual-real fusion experiment in the present invention, conduct experimental research and analysis on system characteristics under high-frequency load fluctuations in polar icebreaking conditions, as well as research on rapid power matching of the system, so as to provide an effective solution for rapid power matching of the system under high-frequency load fluctuations.

[0089] This method combines physical experiments and virtual simulations, especially conducting simulation experiments under complex polar working conditions, enabling in-depth understanding of the actual operation of the icebreaker's thermal system under extreme conditions. This includes not only understanding the performance of individual functional devices but also covering the coupling behavior of the entire system. Compared with traditional methods that rely solely on theoretical analysis or limited on-site tests, this method provides a more comprehensive and accurate insight into the dynamic characteristics of the system. In addition, continuously correcting the virtual model based on physical experiment data ensures the accuracy and reliability of the model. Especially after preliminary verification under typical polar working conditions, further using the corrected model to simulate complex polar working conditions improves the credibility of the prediction results. Compared with existing technologies, this method significantly reduces the errors caused by inaccurate initial assumptions and improves the overall accuracy of the model. Moreover, it can overcome the limitations of experimental tests. Physical experiments are often limited by factors such as site, cost, and safety, making it difficult to fully cover all possible working conditions. Virtual simulation can flexibly adjust parameter settings to simulate various extreme working conditions, making up for the deficiencies of physical experiments. Compared with existing technologies, this method effectively solves the problem of being unable to conduct comprehensive experiments due to resource limitations and achieves extensive testing of the icebreaker's thermal system under different working conditions. In addition, using the corrected virtual system for detailed analysis and evaluation can identify potential problems and improvement points in the system, providing a scientific basis for subsequent design optimization. Compared with traditional methods, this method can not only discover existing problems but also explore the optimal solution by simulating different design schemes, greatly enhancing the efficiency and effectiveness of the design process.

[0090] This method can clearly reveal the dynamic characteristics of the system, improve the accuracy of modeling, overcome the limitations of experimental tests, and provide an effective auxiliary means for system design optimization. It not only makes up for the deficiencies of traditional technologies in dealing with complex polar working conditions but also, through the integration of virtual and physical methods, raises the research, development, and optimization of the icebreaker's thermal system to a new level, providing strong technical support for future Arctic exploration.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Those of ordinary skill in the art should understand that any modifications or equivalent replacements made to the technical solutions of the present invention without departing from the purpose and scope of the present technical solution shall be covered by the scope of the claims of the present invention.

Claims

1. A virtual-real fusion test method for the thermal system of an icebreaker under complex polar conditions, characterized by: The following steps are involved: S1. Based on the composition and structure of the actual icebreaker thermal system, build a physical experimental system of the icebreaker thermal system; S2. Based on the principle of mechanism model, a dynamic model of each functional device in the thermal system of the icebreaker is constructed; and each functional device is used as a basic unit to construct a virtual system of the thermal system of the icebreaker; the composition structure of the virtual system is the same as that of the physical experimental system; S3. Use the physical experiment system to conduct physical experiments under typical polar working conditions; at the same time, use the same typical polar working conditions to conduct simulation experiments on the virtual system; and based on the experimental data of the physical experiment and the simulation experiment, modify the virtual system; S4. Use the modified virtual system to conduct simulation experiments of complex polar conditions and obtain corresponding simulation experiment data; S5. Use the simulation data and experimental data obtained in S3 and S4 to optimize the thermal system design and control strategy of the icebreaker.

2. The virtual-real fusion test method for the thermal system of an icebreaker facing complex polar conditions as claimed in claim 1 is characterized by: In S2, the process of constructing the virtual system of the icebreaker's thermal system includes: Step 2.1, based on the physical test data and mechanism analysis, identify the key working fluid parameters that affect the performance of the thermal system and the operating status of the equipment, and form a working fluid state parameter set that characterizes the dynamic characteristics of the system; through the physical test data and mechanism analysis, select the key parameters that can reflect the preset dimensions to form an equipment state parameter set; the preset dimensions include equipment energy transfer efficiency and fatigue stress; Step 2.2, based on the working fluid state parameter set, a modular modeling method is used to establish a mathematical model for describing the working fluid state parameters and their change process from a preset perspective for each functional device of the thermal system as the corresponding working fluid dynamic model; the preset perspective includes mass, energy, momentum and working fluid properties; Step 2.3, based on the device state parameter set and its changing rules, considering the preset motion form, establish the control equation for each functional device of the non-fluid container, and build the dynamic model of the functional device in combination with the boundary conditions and constraints; the preset operation forms include rotation, translation, and vibration; for each typical fluid container device in the system, build its three-dimensional dynamic model; Step 2.4: Integrate the working fluid dynamic model and the dynamic model, take each functional equipment as the basic unit, integrate the system-level dynamic model of multi-physical field coupling according to the actual working fluid flow path and signal transmission relationship of the thermal system, and use it as a virtual system of the thermal system.

3. The virtual-real fusion test method for the thermal system of an icebreaker facing complex polar conditions as claimed in claim 2 is characterized by: In step 2.1, the process of constructing the working fluid state parameter set includes: Based on the physical test data and mechanism analysis, the core working fluid parameters that affect the output of the thermal system and the operation of the equipment are screened to form a parameter set characterizing the dynamic characteristics of the working fluid; the core working fluid parameters include temperature, pressure, flow rate, and phase change characteristics; Combined with the characteristics of ice-breaking working conditions, the influence of high-frequency load fluctuation on working fluid state parameters is analyzed in detail to obtain a working fluid state parameter set; the high-frequency load fluctuation includes transient flow fluctuation and phase change delay.

4. The virtual-real fusion test method for the thermal system of an icebreaker facing complex polar conditions as claimed in claim 3 is characterized by: In step 2.1, the dimensions of the energy and mass transfer efficiency of the equipment include heat transfer coefficient and mechanical efficiency; the dimensions of fatigue stress include vibration amplitude and thermal stress.

5. The virtual-real fusion test method for the thermal system of an icebreaker facing complex polar conditions as claimed in claim 2 is characterized by: In step 2.2, the working fluid properties include state equation and viscosity model.

6. The virtual-real fusion test method for the thermal system of an icebreaker facing complex polar conditions as claimed in claim 5 is characterized by: In step 2.2, after establishing a mathematical model for describing the working fluid state parameters and their changing process, a high-frequency load dynamic response module is embedded in the mathematical model according to the characteristics of the ice-breaking working condition to characterize the rapid change process of the working fluid parameters; the high-frequency load dynamic response module includes a transient flow control equation and a phase change lag correction term.

7. The virtual-real fusion test method for the thermal system of an icebreaker facing complex polar conditions as claimed in claim 2 is characterized by: In step 2.3, for each typical fluid container device, a fatigue stress model is also introduced to characterize the dynamic stress characteristics of the device under extreme working conditions; the fatigue stress model includes a thermal-mechanical coupling equation.

8. The virtual-real fusion test method for the thermal system of an icebreaker facing complex polar conditions as claimed in claim 2 is characterized by: In step 2.3, for typical fluid container equipment, a model construction method that integrates dimensionality reduction mechanism model and data-driven model is used to construct its three-dimensional dynamic model.

9. The virtual-real fusion test method for the thermal system of an icebreaker facing complex polar conditions as claimed in claim 1 is characterized by: In S3, the correction of the virtual system includes steady-state correction and transient correction; The steady-state correction includes: calibrating the corresponding parameters in the virtual system by comparing the experimental data of the physical experiment and the simulation experiment under typical polar working conditions; Transient correction includes: applying typical transient excitation, verifying the transient response accuracy of the dynamic model, and calibrating the corresponding parameters in the virtual system; the typical transient excitation includes load step change and sudden ice resistance impact; the transient response accuracy includes overshoot and stabilization time.

10. The virtual-real fusion test method for the thermal system of an icebreaker facing complex polar conditions as claimed in claim 1 is characterized by: In S5, the optimization design includes system flexibility structure optimization and system control strategy optimization.

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